📑 Case Studies

🎯 1. Vanity Metrics vs. Decision-Useful Insight

A national media-support programme required grantees to report social-media impressions and press mentions as their main indicators of success. When platform algorithms changed, engagement collapsed—but no audience-research or product-analytics had been funded. Teams lacked evidence to adapt distribution or formats.

Lesson: Prioritising visibility metrics (impressions, mentions) over decision-useful indicators (audience trust, engagement quality, conversion) traps organisations in chasing numbers that don’t reflect public value or sustainability.

Design MEL frameworks that help both donors and outlets answer: What changed? Why? What will we do differently next time?

---

🔐 2. Unsafe Data Practices in Hostile Environments

In a captured-media market, a donor-funded consortium collected detailed staff lists, partner names and GPS-tagged media assets to satisfy audit requirements. When a server was compromised, sensitive data about journalists were exposed. The consortium later adopted data-minimisation, anonymised IDs, tiered access, and offline backups.

Lesson: Safe data handling is part of learning—not an add-on. Under Principle 6, research and monitoring must include ethical, secure, context-sensitive data protocols so that the pursuit of evidence never endangers media partners.

---

🔄 3. From Static Evaluations to Adaptive Learning

A Balkan investigative newsroom persuaded its donor to replace a one-off endline evaluation with quarterly “learning sprints.” Each sprint tested audience responses (e.g., newsletter subjects, video vs. long-form). The team documented results, adapted content, and adjusted its roadmap. Over 12 months, reader time and subscriptions increased, and the donor gained richer insights into what worked.

Lesson: Learning systems should be iterative and adaptive rather than fixed. Embedding short feedback loops turns evaluation into a driver of innovation and sustainability, not a box-ticking exercise.

---

🧾 4. Publishing Redacted Evaluations to Build Trust

A coalition in North Africa agreed to publish evaluation summaries—with names and locations redacted—covering methods, findings, and “what we changed next.” This transparency improved sector learning, attracted new donors, and strengthened trust among audiences and peers.

Lesson: Sharing evaluation results—safely—multiplies learning. Redacted or anonymised public summaries let stakeholders demonstrate accountability and enable peer-to-peer improvement without exposing partners to risk.

---

🧱 5. Building a Data Framework for Journalist Safety

Researchers at the University of Sheffield partnered with Free Press Unlimited to create a methodology for collecting data on attacks and threats against journalists. Their study revealed that existing databases missed most non-lethal attacks and online harassment, skewing understanding of risk. The new framework is now used by several international organisations to design targeted protection measures. 🔗

Lesson: Investing in research infrastructure and standardised data builds the foundation for evidence-based protection policies. Donors should fund longitudinal studies, shared taxonomies, and open data standards for journalism safety.

---

🌍 6. Evaluating Media-Literacy Interventions in Europe

An EU-commissioned study across eight Member States assessed national media-literacy programmes and industry initiatives (including Google’s). It found strong results from multi-sector partnerships and local adaptation, but weak evaluation and poor tracking of behavioural outcomes. 🔗

Lesson: Cross-national research helps identify what works and where. For EU stakeholders, Principle 6 means supporting comparative studies, peer-policy reviews, and shared evaluation frameworks that strengthen evidence for future programming.

---

💬 Field Voices

“We are good at collecting numbers but bad at asking what they mean. Evaluations should explain why an approach worked and how others can use it.” — EU Delegation programme officer (interview, 2025)

“Partners tell us they never see the results of the studies they helped generate. Sharing lessons back is a sign of respect, not a luxury.” — Local media organisation, Eastern Europe consultation

---

✅ Summary — Key Takeaways for Implementers

• Replace vanity metrics with value metrics. Track trust, reach, retention—not just clicks.

• Design safe learning systems. Protect people and data while collecting evidence.

• Embed learning loops. Use quarterly or mid-term reflections instead of one-off evaluations.

• Share knowledge back. Translate, redact, and disseminate lessons to the partners who generated them.

• Invest in research ecosystems. Support universities, observatories, and data collaboratives that sustain long-term insight.

• Coordinate and store. Build joint repositories so that every evaluation feeds future programme design.